Health care facilities of our rural India are in a state of utter indigence. Over three-fifths of those who live in rural areas have to travel more than 5 km to reach a hospital and the health care services is becoming out of reach for the economically backward society of India. Currently, as the rural community experiences about 22.9% of death due to heart diseases , there is a need to improve the remote ECG monitoring devices to cater the needs of rural India. The existing wearable ECG devices experience several issues to accurately detect the type of heart diseases due to the presence of motion artifacts, and to warn the doctor during critical conditions. Hence, even though wearable devices are finding their place in today’s healthcare systems, the above mentioned issues discourages a doctor in depending upon it. So to enhance the existing wearable ECG device, a context aware system was designed to collect the BMA (Body Movement Activity). In this research work an innovative BMA classifier has been designed to classify the physical activities of users, from the real-time data received from context aware device. The test results of the BMA classifier integrated with the complete system shows that algorithm developed in this work is capable of classifying the user activity such as walking, jogging, sitting, standing, upstairs, downstairs, and lying down, with an accuracy of 96.66%.
Shereena Shaji, Dr. Maneesha V. Ramesh, and Menon, V. N., “Real-time Processing and Analysis for Activity Classification to Enhance Wearable Wireless ecg”, in Proceedings of the Second International Conference on Computer and Communication Technologies, 2016.